In real-world scenarios, corrupted images usually contain mixture and complex degradations (e.g., blur, noise and JPEG compression).
Such a complex real-world degradation process can be simulated by a random shuffle order [1]} or a high-order degradation process [2]}. Due to the mixtures of different degradation levels and complicated process of different degradation types in high-order degradations, the explicit degradation levels can not reflect the actual degradation effects in the corrupted images. For example, a corrupted image is degraded by a sequence of Gaussian blur with \(\sigma _1\) , Gaussian noise, and Gaussian blur with \(\sigma _2\) . We cannot know the final equivalent blur level on this degraded image.
This case becomes more complicated when the real-world degradation process involves more degradation types and more complex degradation combinations.
Therefore, existing modulation-based methods trained with known degradation types and levels can not get effective supervision in real-world settings.
How to extend the interactive modulation ability to RWSR is worth investigating and has not yet been studied.
